Systems and methods for grid-based insurance rating

ABSTRACT

A system and method for underwriting and rating insurance products using a programmed computer system to receive usage information of a vehicle within a geographic area or location, determine a target grid cell or a sequence of target grid cells bounded by latitude and longitude lines that encompass at least a portion of the geographic area where the vehicle was used, attain a set of data associated with the target grid cell(s), and calculate a location rating factor based on the usage information, target cell(s), and the set of data.

TECHNICAL FIELD

The present disclosure relates generally to systems and methods forunderwriting an insurance policy for a vehicle. These systems andmethods may utilize computer, hardware, software, and data stores togather and process data to determine rating factors for grid locationsassociated with a geographic area where the vehicle is used.

BACKGROUND

Insurance underwriting is the process of assessing the value of a givenrisk, and in turn pricing a policy to protect against that risk.Fundamentally, insurance premiums are designed to reflect the amount ofa payout should a covered event occur in view of the likelihood of theoccurrence of that event. The process of determining the cost of aninsurance policy is called rating. The rating process may include anumber of variables, including experience data for a specific insured,experience data for a class of insured entities, capital investmentpredictions, profit margin targets, and a wide variety of other datauseful for predicting the occurrence of certain real-world events aswell as the amount of damage likely to result from such events.

Further, “experience rating” involves analyzing past claims experienceto determine a prospective premium amount and/or a retrospective premiumadjustment. See, e.g., P. Booth, “Modern Actuarial Theory and Practice,”340-51 (Chapman & Hall/CRC 1999). For example, a business may operate alarge fleet of vehicles. And, that business may seek to insure thevehicles to cover property damage and to cover possible personal injuryclaims if a fleet vehicle were to be in an accident with anothervehicle. If the fleet is large enough or the business has been operatingthe fleet long enough, there may be enough historical data to reliablyand accurately estimate the expected claims for the next year. Thatestimate (possibly combined with an allocation of expenses or assessmentof an administrative fee) would represent the insurance premium in anideal scenario. At the end of the annual policy term, a surcharge orrefund may also be appropriate if the actual claims for the term werehigher or lower than the estimated claims amount.

A typical family seeking automobile insurance cannot, however, produceanywhere near the amount of data needed to make a reliable and accurateestimate of anticipated claims for their vehicle or vehicles. Thus,insurance companies must rate personal policies in a risk pool ofcomparable policies to produce enough data to make such an estimate. Onemechanism for doing this is to assess what data is available for thefamily (e.g., demographic information, types of vehicles, and whatlimited claim information is available) and use that data to assign anappropriate pool to the family.

The myriad types of data available to an insurer for performing therating process are often associated with geographic locations orregions. However, this association is not consistent or uniform. Someproperty crime data is associated with a “block” of addresses on a citystreet, e.g., 300-400 block of Main Street. Flood zone data and landelevation data may be stored as complex topographic maps. Lossexperience data may be associated with a coordinate pair representingthe longitude and latitude of the location of the loss event.

At present, insurance rating requires a complex search process tocompile relevant data for input into a rating function. For example, apolicy to be rated may be associated with a specific location, e.g., astreet address of a home or office or the location where a vehicle willbe parked at night. To rate a policy for that location, some subset ofthe relevant data must be gathered and provided to a rating algorithm.The gathering process is often computationally difficult in view of theinconsistent and non-uniform associations of data to geography discussedabove. In some instances, data is processed and aggregated by county,city, and/or postal ZIP code. This aggregation is made difficult by thenearly arbitrary boundaries defined by county lines, city limits, andZIP codes. Further, county, city, and postal ZIP code boundaries maychange over time. In other instances, data is processed by aggregatedsales territory.

Rates appropriated to each area are generally determined based on theassociated historical claims experience. While existing methods ofterritorial rating have served insurance providers well, theseapproaches can be problematic for several reasons: (1) geographicboundaries can change, as discussed above; (2) geographic areas may belarger than desired; (3) populations may not be equally distributedwithin these geographic areas; (4) historical claim experience withinthese geographic areas may be limited; and (5) where a vehicle isgaraged does not accurately measure geographic risk of where the vehicleis used.

SUMMARY

In accordance with the teachings of the present disclosure,disadvantages, and problems associated with existing insurance ratingsystems have been reduced.

According to one aspect of the invention, there is provided a method forrating insurance products using a programmed computer system. The methodcomprises receiving usage information of the vehicle within thegeographic area, determining each of a plurality of coordinate gridcells or blocks, e.g., target grid cells or target cells, thatencompasses at least a portion of the geographic area where the vehiclewas used, querying a database to attain a set of data associated witheach of the determined plurality of grid cells that encompasses at leasta portion of the geographic area where the vehicle was used, receivingthe queried set of data associated with each of the plurality of gridcells that encompasses at least a portion of the geographic area wherethe vehicle was used, calculating a location rating factor based on theusage information and the received set of data associated with each ofthe plurality of grid cells that encompasses at least a portion of thegeographic area where the vehicle was used, and communicating thecalculated the location rating factor to a user.

According to another aspect of the invention, there is provided a methodfor rating insurance products using a programmed computer system. Themethod comprises storing data associated with a plurality of grid cellsor blocks corresponding to a geographic area, receiving a query for avehicle insurance rate for the vehicle, and receiving usage informationof the vehicle within the geographic area, wherein the usage informationincluding a route travelled by the vehicle. The method further includesdetermining a sequence of grid cells, e.g., target grid cells or targetcells, that encompasses the one or more routes travelled by the vehicle,querying a database to attain a set of data associated with each of thegrid cells within the sequence of grid cells that encompasses theroute(s) travelled by the vehicle, receiving the queried set of dataassociated with each of the plurality of grid cells within the sequenceof grid cells that encompasses the route(s) travelled by the vehicle,calculating a location rating factor based on the usage information andthe received set of data associated with each of the grid cells withinthe sequence of grid cells that encompasses the route(s) travelled bythe vehicle, and communicating the calculated location rating factor toa user.

According to a further aspect of the invention, there is provided acomputer system for underwriting and rating insurance products. Thecomputer system comprises a processor communicatively coupled to a userinterface, a coordinate grid system associated with a geographic area,wherein the coordinate grid system includes a plurality of grid cells orblocks and each grid cell comprising a four-sided area defined bylatitude and longitude values. The computer system further includes adatabase communicatively coupled to the processor, wherein the databasestores data associated with the plurality of grid cells, and a positionmonitoring device communicatively coupled to the processor formonitoring usage of a vehicle. The computer system further includes anon-transitory, tangible computer readable memory communicativelycoupled to the processor; and a set of computer readable instructionsstored in the non-transitory computer readable memory, which whenexecuted by the processor, are configured to: receive a query for avehicle insurance rate for the vehicle, receive usage information of thevehicle from the position monitoring device; determine each of theplurality of grid cells, e.g., target grid cells or target cell, thatencompasses at least a portion of a geographic location where thevehicle was used, query the database to attain a set of data associatedwith each of the determined grid cells that encompasses at least aportion of the geographic area where the vehicle was used, receive thequeried set of data associated with each of the plurality of grid cellsthat encompasses at least a portion of the geographic area where thevehicle was used, calculate a location rating factor based on the usageinformation and the received set of data associated with each of theplurality of grid cells that encompasses at least a portion of thegeographic area where the vehicle was used, and communicate thecalculated location rating factor to a user.

In further accordance with any one or more of the foregoing exemplaryaspects or embodiments, a system, method, and/or computer-readablemedium may further include any one or more of the following preferredforms.

In one preferred form, the method includes monitoring, via a positionmonitoring device, usage of the vehicle within the geographic area.

In another preferred form, the usage information of the vehicle includesone or more of the following: location, miles driven, moving time,non-moving time, total time within one of the plurality of grid cells,time when vehicle ignition is on, time when vehicle ignition is off, andvehicle velocity.

In another preferred form, the set of data includes one or more of thefollowing: census data, crime data, weather data, historical data, andother data, such as quantity of vehicle insurance claims, severity ofvehicle insurance claims, frequency of vehicle insurance claims, policereports, driving statistics, road statistics, time, date, and populationdensity.

In another preferred form, the location of the vehicle includes acoordinate pair comprising a longitude value and a latitude value.

In another preferred form, each of the grid cells of the plurality ofgrid cells includes a four-sided area defined by latitude and longitudevalues of a coordinate grid system defining a geographic size of thatparticular coordinated grid cell.

In another preferred form, the method includes adjusting the geographicsize of one or more coordinate grid cells by truncating the number ofdigits in the latitude and longitude values that define that grid cells.

In another preferred form, the method includes automatically comparingthe usage information of the vehicle to usage criteria of a vehicleinsurance policy; automatically detecting a discrepancy between theusage information of the vehicle and the usage criteria of the vehicleinsurance policy; and automatically notifying a user, for example, aninsurance agent or an owner of the vehicle, of the detected discrepancybetween the usage information of the vehicle and the usage criteria ofthe vehicle insurance policy.

In another preferred form, the method includes querying, via theprocessor, a database to attain a supplemental set of data associatedwith grid cells adjacent to the sequence of target grid cells thatencompasses the one or more routes travelled by the vehicle (forexample, a border of grid cells along the sides of the sequence oftarget grid cells). The method further includes receiving, at theprocessor, the queried supplemental set of data associated with gridcells adjacent to the sequence of grid cells that encompasses theroute(s) travelled by the vehicle, and calculating, via the processor, alocation rating factor based on the usage information, the received setof data associated with each of the grid cells within the sequence ofgrid cells that encompasses the route(s) travelled by the vehicle, andthe received set of supplemental data associated with grid cellsadjacent to the sequence of grid cells that encompasses the route(s)travelled by the vehicle.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the present embodiments and advantagesthereof may be acquired by referring to the following description takenin conjunction with the accompanying drawings, in which like referencenumbers indicate like features, and wherein:

FIG. 1 illustrates a stylized town map overlaid with a coordinate grid,according to certain embodiments of the present disclosure.

FIG. 2 illustrates a stylized neighborhood map with two differentgridline representations, according to certain embodiments of thepresent disclosure.

FIGS. 3A-3C illustrate three variations of a process of gatheringprogressively larger amounts of geographically relevant data from GRIDcells.

FIG. 4A illustrates a stylized town map superimposed with ringsaccording to certain embodiments of the present disclosure.

FIG. 4B illustrates a stylized town map superimposed with a borderaccording to certain embodiments of the present disclosure.

FIG. 5 illustrates the stylized town map overlaid with a coordinate gridand an alternative set of geographic boundaries, namely ZIP codes.

FIG. 6 illustrates various approaches to mapping data into a grid-basedrepresentation scheme.

FIGS. 7A-7B illustrate processes for coordinating data with a GRID mapaccording to an embodiment of the invention is disclosed according to aflow chart.

FIG. 7C illustrates an example of graphical information to be mappedinto a GRID-based data structure.

FIG. 8A provides a flow chart, which is illustrative of an embodiment ofthe invention.

FIG. 8B provides a flow chart, which is illustrative of anotherembodiment of the invention.

FIG. 9A provides representative GRID distance weighting values, for anautomobile example.

FIG. 9B provides an overview of the methodology for an automobileexample.

FIG. 9C provides the GRID ring level data for an automobile example.

FIG. 9D provides the GRID cell level data for an automobile example.

FIG. 9E provides methodologies for calculating distance between twolatitude and longitude coordinate pairs in an automobile example.

FIG. 9F provides the results of the distance calculations for anautomobile example.

FIG. 10 illustrates a computing and information handling systemaccording to one embodiment of the invention.

DETAILED DESCRIPTION

Preferred embodiments and their advantages over the prior art are bestunderstood by reference to FIGS. 1-10 below. However, the presentdisclosure may be more easily understood in the context of a high leveldescription of certain embodiments.

As discussed in the BACKGROUND, insurance risks are often rated based ondata relevant to one or more particular geographic locations. Forexample, a vehicle insurance policy may represent a risk that variesrelative to the location where a vehicle is parked or driven. If thevehicle is driven or parked on the street in an urban neighborhood, therisk of damage may be significantly higher than if the vehicle is drivenin a rural area or parked in a secure garage. For the purposes of thisdisclosure, the term target location or target cell will be used toreference a specific geographic location relevant to rating of aspecific covered risk. In some circumstances, there may be multiplerelevant geographic locations (e.g., a garage location and a worklocation for a vehicle, or a sequence of locations such as one or moreroutes travelled by a vehicle), and each location may be treatedindependently, collectively, and/or in some combination thereof.

One aspect of the invention is to utilize territories defined bylatitude and longitude coordinates. This technique allows insuranceproviders to more finely segment policy pricing based on geographiccharacteristics and provides more pricing points than currentstructures. For purposes of this application, the system is calledGeographic Rating Identification (GRID) Based Rating and utilizeslatitude and longitude coordinates to determine a location rating factor(LRF) that may apply to a policy. An LRF represents geographic risk ofthe insurance policy. The GRIDs may be set up so that the individualtarget cells are not equal in size according to area. Rather, thelatitudinal and longitudinal coordinates may be truncated at varyingprecisions to provide individual target cells of different sizesaccording to area. For example, in urban, populated areas, the GRID maybe smaller, while in rural, less populated areas the GRID may be muchlarger. Once the GRIDs are established, then individual policies may bequickly assigned to GRIDs by looking up the latitudinal and longitudinalcoordinates of the associated usage information of a vehicle. Truncationof the latitudinal and longitudinal coordinates may provide for muchquicker lookup functionality.

Latitude and longitude coordinates for particular geographic locationsmay be obtained via a geocoding process or service, wherein the latitudeand longitude coordinates are provided with a precision to the sixthdecimal place for both values along with codes indicating the quality ofthe result. Results at that level of precision identify a geographicpoint at the sub-inch level.

Latitude and longitude coordinates may be used to define a coordinatesystem using four defined corners and developing a database table andlookup methodology, wherein each GRID cell area is of similar geographicsize. The complexity of the queries required to retrieve results fromthe developed tables makes this solution less preferred.

Alternatively, a database function may be used to group geographicallylike risks. Typically, these areas are not equal in geographic size. Thegrouping may use a single database function to provide significantlyfaster results. This method also could be easily integrated withexisting database systems used for the rating process and can moreeasily connect the pieces to form a countrywide map.

According to a further alternative, latitude and longitude coordinatesare used to define GRID cells of different sizes. Because the latitudeand longitude coordinates provide precision to the sixth decimal place,larger GRID cells may be defined by truncating the latitude andlongitude coordinates to fewer decimal places. Thus a single databasemay accommodate variable GRID cell sizes wherein larger GRID cells aredefined by latitude and longitude coordinates that have fewer decimalplaces and smaller GRID cells are defined by latitude and longitudecoordinates that have more decimal places.

Some benefits of GRID Based Rating include: (1) latitude and longitudecoordinates are fixed and do not change over time (unlike ZIP codes orother geographic boundaries); (2) rates are based on loss experience inthe target GRID cell and nearby GRID cells, as opposed to large areascovering multiple ZIP codes or counties with portions remote from thelocation of interest; (3) improved matching of price to risk overcurrent territorial rating; (4) promotes smoother transitions fromhigher cost areas to lower cost areas; (5) can utilize data external toinsurer database information, such as historical meteorological data,road statistics, etc.; and (6) accurate vehicle usage information withlocation rating factors.

According to this disclosure, GRID cells, which may also be referencedto as coordinate grid cells, grid cells, cells, coordinate grid block,grid blocks or blocks; are defined as non overlapping geographic areasdefined by truncated latitude and longitude values, wherein the size ofthe GRID cells can vary depending on the level of truncation precision.A truncation example is provided that illustrates how the level oftruncation can be used to capture more or fewer point locations, e.g.,geographic locations, in a given GRID cell. The example in TABLE 1 hasnine point locations that exist at the noted latitude and longitudecoordinates. Each point is lettered and is accurate to six positions tothe right of the decimal for both latitude and longitude.

TABLE 1 Point Name Latitude Longitude Premium A 42.221623 −80.241321 145B 42.224237 −80.248427 225 C 42.226772 −80.242043 412 D 42.229623−80.242843 299 E 42.228791 −80.247182 305 F 42.228423 −80.238221 205 G42.228911 −80.251822 335 H 42.230879 −80.249142 289 I 42.225291−80.236382 435

To develop a summarized amount of premium for a particular area (GRIDcell), the absolute value of latitude and longitude for each point maybe truncated at two decimal positions and grouped in a query of thedatabase. These summaries are listed in TABLE 2.

TABLE 2 Point Name Latitude Longitude Premium A 42.22 80.24 145 B 42.2280.24 225 C 42.22 80.24 412 D 42.22 80.24 299 E 42.22 80.24 305 F 42.2280.23 205 I 42.22 80.23 435 G 42.22 80.25 335 H 42.23 80.24 289

With this level of truncation, the nine points fall into four differentGRID cells, which are bounded by two decimal point precision latitudeand longitude lines. Next, the points may be grouped according to thetruncated latitude and longitude, as shown in TABLE 3.

TABLE 3 Latitude Longitude Total Premium 42.22 80.24 1386 42.22 80.23640 42.22 80.25 335 42.23 80.24 289

The latitude and longitude coordinates are components of a uniqueidentifier, GRID ID, which may be used to identify each GRID cell. TheGRID ID may be determined by first taking the absolute value of thelatitude and longitude coordinates, truncating the coordinates to aspecified number of digits, eliminating the decimal point, and finallyconcatenating the resulting values together. The specified digitsinclude three digits to the left of the decimal place in order toaccommodate up to 180 degrees of longitude. The GRID ID matches thelatitude and longitude of the lower right corner (the Southeast corner)of the GRID cell. The following transformation may be implemented insoftware as a grid cell determination means.

Latitude Longitude GRID ID 42.22 80.24 → 0422208024

This example uses a single attribute to demonstrate the grouping, butincurred losses or other factors could also have been used. Thetruncation method can be used as part of an actuarial flow where totalpremium is compared to total losses and other factors to develop anappropriate rate for a grid area. The GRID ID may be represented as asingle numeric value, e.g., 0422208024, or may be represented by itscomponent parts, e.g., 04222 and 08024. While the examples provided showlatitude and longitude represented in decimal format, other formats canprovide the same function including, for example, degrees, minutes, andseconds.

At this point in the disclosure, reference to the figures may behelpful.

FIG. 1 illustrates a stylized town map overlaid with a coordinate grid,according to certain embodiments of the present disclosure. This mapprovides some context for the Map 100 and includes a 7×9 array ofcoordinate grid cells or blocks, locations A-F, river 101, highway 102,surface streets 103, neighborhoods 110, 111, and 112, and an exampleroute 125 (shown in dotted line with arrowheads) travelled by a vehicle.It is to be understood that the term “vehicle” includes all types ofvehicles, including, and not limited to, automobile, motorcycle, truck,bus, personal transporter (e.g., electripod), and boat. The coordinategrid includes gridlines at hundredths of a degree from 30.420N to30.429N and from 97.270W to 97.277W. Note that a reference to acoordinate grid cell at (X, Y) will refer to the cell with a lower rightcorner at coordinate (X, Y). For example, highway 102 crosses river 101in grid cell (97.272W, 30.425N). Locations A-F are each associated witha latitude/longitude coordinate pair. Each location A-F may represent areal property location to be insured, a physical location where personalproperty may be garaged or kept, or the location of historical datapoint (e.g., claim or crime scene).

River 101 is a body of water separating neighborhood 111 from 110 and112. River 101 may represent other geological or geographical barrierslike steep inclines or ravines. River 101 may represent a rail line orlimited access roadway. These barriers may represent disparate data setswhere, for example, neighborhood 110 may represent a collection ofhomes. In contrast, neighborhood 112 may include a mix of commercial andresidential properties. Finally, neighborhood 111 may be an urban citycenter with high-rise offices and residences. When rating a vehicleinsurance policy for a vehicle associated with location E (denoted by asquare), data from locations where the vehicle is used (for example,travel route 125 traversing grids associated with locations C, F, and B)may be more relevant from an insurance rating perspective than data fromwhere the vehicle is garaged, e.g., location E. This may be the caseeven though location B is farther from where the vehicle is garaged thanlocation D.

In some embodiments, river 101 may represent a political border (e.g.,between states or countries). Because insurance is regulated bypolitical entities, namely each of the fifty United States plus theDistrict of Columbia, data may be compartmentalized with a state'sborders. This compartmentalization may be for accounting purposes andmay be required by law or regulation.

Highway 102 is a major vehicle artery cutting through the center of thetown and spanning river 101. Highway 102 may present a similar barrieras river 101. Highway 102 may also provide access to emergency servicessuch as fire or police departments. Surface streets 103 are roadways,paths, or other means for surface transportation.

The exemplary route 125 that the vehicle may travel includes one or moresurface streets 103 and the highway 102 for travelling from location Eto location B (denoted by a square). The route 125 may be a commutingroute or a delivery route. When rating a vehicle policy, data fromlocations, roads, neighborhoods, etc., encompassing or near the vehicleduring its use may be more relevant than data from locations, roads,neighborhoods, etc., that the vehicle does not use or are not near.

FIG. 2 illustrates a stylized neighborhood map with two differentgridline representations, according to certain embodiments of thepresent disclosure. Map 200 includes properties 201, roads 202, 203, and204, fire station 205, fire hydrant 206, equidistant gridlines 210, 211,212, and 213, and coordinate gridlines 214, 215, 216, and 217.

Properties 201 represent real estate structures subject to insurancerating, subject to insurance claims, and the locations forinsurance-related events. Properties 201 are illustrated as detached,single-family residences, but may be any sort of real property. Forexample, some of these properties may be multi-family properties such asduplexes, triplexes, or condominiums. Some properties may be adjoiningtown homes or row houses. Some properties may be mid-rise or high-riseapartments or condominiums. Some properties may be zoned forresidential, commercial, industrial, or municipal use. Some propertiesmay be unimproved land in a natural state or subject to some other use.Properties 201 need not be homogenous as a single-family detached homeand may be located next to a condominium project or next to a retailestablishment.

Roads 202, 203, and 204 connect properties 201 to allow transportationof people, items, and vehicles between or past those properties. Roads202, 203, and 204 are illustrated as uniformly sized residential roads,but may be any sort of road. Associated with the roads may be variousroad statistics, such as paved, unpaved, number of lanes, number ofintersections, speed limit, lighting, guard rails, road grade,maintenance, average number of vehicles that travel the road at varioustimes of day, distance to hospital, equipped with vehicle communicationscapability, etc. Further, some roads may be paved with asphalt, brick,concrete, or stone. Some roads may be made from gravel. Some roads maybe dirt roads. Some roads may be pedestrian walkways or bike paths. Someroads may include dedicated lanes for motor vehicles and bicycles. Roadsmay have any number of lanes and may have painted stripes demarkinglanes of traffic. The intersections of roads 202, 203, and 204 mayinclude traffic control devices such as yield signs, stop signs, ortraffic lights.

Fire station 205 and fire hydrant 206 represent insurance-relatedcommunity services. Other services may include police stations andhospitals. Proximity to insurance-related community services may affordsome protection against certain types of loss or may reduce the likelyimpact of certain events. For example, a nearby community police stationmay reduce the likelihood of certain property crimes. In anotherexample, a nearby fire station may reduce the response time foremergency medical or fire assistance, thereby reducing the likelihood ofmedical complications or total loss of a property to fire.

Equidistant gridlines 210, 211, 212, and 213 illustrate one datasegmentation approach, wherein a map is overlaid with a set of virtualgridlines spaced an equal distance from each other in a given direction.In this case, gridline 210 is 1000 m West of gridline 211 and gridline212 is 1000 m North of gridline 213. This one kilometer grid (or a onemile grid) provides a well understood and easy to illustratesegmentation of zones on a map. In particular, equidistant gridlines210, 211, 212, and 213 define GRID cell 220, which is roughly one squarekilometer. One drawback of the equidistant grid is that any data pointassociated with a coordinate pair—defined as (latitude, longitude)—mustbe mapped into this equidistant grid system prior to analysis with otherdata points within the grid cell. For example, if property 201 atlocation (30.426N, 97.749W) is the subject of a rate quote, the ratingsystem must first determine that location coordinate pair falls withinGRID cell 220. Then the rating process may proceed.

Coordinate gridlines 214, 215, 216, and 217 illustrate another datasegmentation approach, wherein a map is overlaid with a set of gridlinesfollowing longitudinal and latitudinal lines. (The term coordinate gridis used here to refer to latitude/longitude grid used by, for example,the United States Geological Survey.) The coordinate gridlines 214 and215 follow the 97.75W and 97.74W longitudes, respectively. Thecoordinate gridlines 216 and 217 follow the 30.43N and 30.42N latitudes,respectively. These coordinate gridlines are spaced at two decimalplaces of the latitude/longitude coordinates, but other spacing may beappropriate as will be discussed below. In some embodiments, a variablespacing may be used wherein some data is accessed or processed accordingto a narrower spacing than other data. In certain embodiments, two ormore sets of coordinate gridlines may be used for certain types of dataor certain locations, e.g., where data density may vary significantly.In one example, property and/or vehicle related damage experience datamay be dense in an urban location while climate data may be relativelysparse, suggesting that property and/or vehicle related damageexperience data may be queried using a smaller coordinate cell size thanclimate data. In another example, property and/or vehicle related damageexperience data may be sparse in a rural farming community suggestingthat damage experience data may be queried using a larger coordinatecell size than in a dense urban situation.

The curvature of the illustrated coordinate gridlines is exaggerated toillustrate one major difference between this gridline system and theequidistant gridline system. Rather than form a square, the coordinategridlines 214, 215, 216, and 217 form an oblong GRID cell 230, roughly1100 meters tall following a longitudinal cross-section and roughly 960meters across following a latitudinal cross-section. These distances arecalculated based on the well-documented Haversine formula—though otherformulas may be employed. This oblong shape will be more pronounced athigher latitudes, e.g., at 64.72N in Fairbanks, Ak. where the sametwo-decimal grid cell would be roughly 1110 meters by 480 meters. Insome embodiments, the coordinate gridlines used at such extreme northernlatitude may be adjusted to more closely approximate a square grid cell.This may be accomplished, for example, by defining a grid cell withcorners at (64.72N, 147.48W) and (64.73N, 147.50W), or incrementing thelatitude by 0.01 degrees while incrementing the longitude by 0.02degrees.

Data Aggregation in the Ratemaking Process

GRID Based Rating utilizes latitude and longitude coordinates toestablish a network of small territories or “GRID cells” or grid cellsacross a state or region. Each GRID cell represents a defined area, withcorner points defined in terms of truncated latitude and longitude. Eachvehicle insurance policy may be assigned to one or more GRID cells basedon its latitude and longitude coordinates, which are determined based onusage of the vehicle by using reference or target locations associatedwith one or more routes that a vehicle may travel. It is to beunderstood that the present invention is applicable to more than justvehicle insurance and may be applied to others transportation means,e.g., motorcycles, trucks, boats, buses, and other forms of property andcasualty insurance where GRID usage data may be captured.

The premium and loss experience used to calculate policy pricing may bederived from data associated with a target cell(s)—or the GRID cell(s)or grid cell(s) that encompasses the target location or route—as well asdata, e.g., supplemental data, associated with surrounding, bordering,adjacent, or proximate GRID cells. The aggregate risk exposure isdetermined based on the data associated with each target cell and theimmediately surrounding GRID cells. Certain embodiments incorporate aniterative search process using progressively larger data sets with eachdata set incorporating data associated with GRID cells that areprogressively further from the target cell. This method may ensure thatthe most geographically relevant data is included in the calculationsfor each target location. GRID Based Rating uses this data to derivelocation rating factors (LRFs) for each GRID cell.

FIGS. 3A-C illustrate three variations of a process of gatheringprogressively larger amounts of geographically relevant data from GRIDcells. These figures are oriented with the top of the page representinggeographic North and the right side of the page representing geographicEast.

FIG. 3A illustrates a process of gathering progressively larger amountsof geographically relevant data using concentric rings around a targetGRID cell. FIG. 3A includes a target cell, ring 1 surrounding andcontiguous with the target cell, and ring 2 surrounding and contiguouswith ring 1. The number of rings and the size of each ring may vary toaccommodate different data requirements, geographic anomalies, etc. Insome embodiments, each ring includes an equal number of grid cells alongboth the North/South cross-section as with the East/West cross-section.In other embodiments, more or fewer grid cells may be included across aNorth/South cross-section than are included across an East/Westcross-section in order to more closely represent equal geographicdistance in each direction. Two rings are illustrated, but more or fewerrings may be utilized. In some embodiments, the target cell may bereferred to as ring 0.

According to one aspect of the invention, there is a method having thefollowing steps: collecting data for the target cell and determiningwhether the current data collection has enough historical data toprovide actuarially credible results. If not, an iterative process isperformed including collecting data from rings of GRID cells immediatelysurrounding the cell(s) for which data has been collected and evaluatingat each iteration whether enough historical data has been collected.Once a credible set of data has been collected, distance weighting andcredibility weighting are applied and a pure premium is calculated.

FIG. 3B illustrates an alternative process of gathering progressivelylarger amounts of geographically relevant data using concentric cellsaround a target cell. FIG. 3B includes a target cell, a block 1 of cellssurrounding and centered on the target cell, and a block 2 of cellssurrounding and centered on the target cell. While blocks 1 and 2 areillustrated as squares, each may be a rectangle to compensate forrectangular GRID blocks at high or low latitudes.

In certain embodiments, the iterative process first gathers data fromthe target cell and determines if additional data is needed to get acredible data set. If so, data is gathered from block 1 (includingduplicate data from the target cell) and a distance weighting isapplied. In certain embodiments, duplicate data may be explicitlyexcluded. In other embodiments, the duplicate data is left in the setfor computational efficiency and any skew is lessened through theapplication of the distance weight. This process iterates until asufficient amount of data has been collected to satisfy a credibilitythreshold.

In certain computational environments, the process illustrated in FIG.3B may be preferable as it may be simpler and/or faster than the processillustrated in FIG. 3A because data may be queried with two boundarypoints (representing a rectangle) rather than a series of boundarypoints representing the more circular rings illustrated in FIG. 3B.

FIG. 3C illustrates an alternative process of gathering progressivelylarger amounts of geographically relevant data using bordering cells orblocks around a sequence of target cells. FIG. 3C includes a sequence oftarget cells that may be representative of a route travelled by avehicle, border 1 surrounding and centered on the sequence of targetcells, and border 2 surrounding and centered on the sequence of targetcells. Alternatively, borders 1 and 2 may be determined using one of theprocesses illustrated in FIGS. 3A and 3B. In particular, one or morerings surrounding each target cell within the sequence of target cells,e.g., route, may be determined and the rings of each target cell may becompiled to determine one or more borders along the perimeter of theroute.

In certain embodiments, the iterative process first gathers data fromthe sequence of target cells and determines if additional data is neededto get a credible data set. If so, data is gathered from border 1(including duplicate data from the sequence of target cells) and adistance weighting may be applied. In certain embodiments, duplicatedata may be explicitly excluded. In other embodiments, the duplicatedata is left in the set for computational efficiency and any skew islessened through the application of the distance weight. This processiterates until a sufficient amount of data has been collected to satisfya credibility threshold.

FIG. 4A illustrates a stylized town map superimposed with ringsaccording to certain embodiments of the present disclosure. Map 400illustrates the same points of interest as FIG. 1 as well as target cell401 and rings 402, 403, and 404. In this illustration, the target cell401 includes a portion of the highway 102 of the route 125 travelled bythe vehicle. If the standard ring-based method is utilized on the dataset of this particular target cell 401, data associated with location Cmay carry more weight in the calculation of the location rating factorthan data associated with garage location E because location C is in thefirst ring (ring 402) and location E is in the third ring (ring 404). Inother words, the distance weighting will reduce the weight accorded todata associated with location E as compared to location C. In someembodiments, data may be associated with attributes. For example, policereports of numerous vehicle collisions at location C may be associatedwith an attribute of “traffic accidents.” In another example, policereports of numerous vehicle thefts at location B may be associated withan attribute of “crime rate.” In some embodiments, the query for data ina given cell or ring may include a Boolean filter to restrict results,e.g., “NOT ‘vehicle collisions’” or “AND ‘crime rate.’” In someembodiments, a post processing step may apply a lesser weight to resultsnot associated with “crime rate.”

FIG. 4B illustrates a transverse route 425 travelled by a vehiclethrough a mapped stylized town superimposed with borders according tocertain embodiments of the present disclosure. In addition to the samepoints of interest illustrated in FIG. 1, Map 400 in FIG. 4B illustratesa sequence of target cells 405 encompassing the route 425 and asurrounding border 406. If the standard border-based method is utilizedon this data set, data associated with location C may carry more weightin the calculation than data associated with location D because locationC is in the sequence of target cells 405 and location D is in the border406. The distance weighting will reduce the weight accorded to dataassociated with location D. Because some locations are more similar toother locations, certain techniques may be applied to filter the databased on relevancy criteria. In some embodiments, data may be associatedwith attributes. For example, a claim for vehicle related damage atlocation E may be associated with an attribute of “local driving.” Inanother example, a claim for vehicle related damage on the highway 102may be associated with an attribute of “interstate driving.” In someembodiments, the query for data in a given cell or border may include aBoolean filter to restrict results, e.g., “NOT ‘highway’” or “AND‘surface streets.’” In some embodiments, a post processing step mayapply a lesser weight to results not associated with “surface streets.”

Data Relevant to the Ratemaking Process

With an understanding of the generalized data gathering process, someelaboration is necessary as to the types of data relevant to ratemaking.In certain embodiments, one step of the method is to collect historicaldata on a given target GRID cell and all nearby GRID cells within aspecified radius where the vehicle is used. In this step, an exposureadjustment may also be applied by peril (e.g., fire, crime, and otherextended coverage). Data may be collected from a variety of sources bothinternal and external to the insurance provider. For example, data maybe collected regarding fire station and fire hydrant locations andcharacteristics, weather data, government data, in particular census,tax, population, traffic, employment, businesses, crime statistics,soil, vegetation, flood plains, burn zones, etc.

Census data may be collected and include: population density, averagenumber of vehicles per household, average travel time, and travel type(drive, car pool, public transportation, etc.). Census data may beobtained from third party vendors or other external sources. Firestation data may include: distance to responding fire station, distanceto nearest fire station, fire station type (paid, volunteer,combination, other), and fire station characteristics (trucks,equipment, water supply, etc.). Fire Station data may be obtained fromthird party vendors or other external sources. Crime data may include:robbery counts, burglary counts, larceny-theft counts, motor vehicletheft counts, and arson counts. Brush fire data may include: brush firepotential, and vegetation index. Weather data may include: averagenumber of hail events, average hail stone size per event, average numberof tornado events, average tornado rating per event, average tornadolength per event, average tornado width per event, average annualrainfall, average annual snowfall, average high temperature, average lowtemperature, and frequency of weather watches and warnings issued. Otherdata may include: traffic density, average driving distance, earthaspect (measures the amount of sunlight at a location), slope, faultlines, and soil type.

Telematic devices may also be attached to vehicles to collect usageinformation. For example, a position module, such as a global positionsunit (GPS), may be attached to a vehicle to collect vehicle operationdata, which may include: location, miles driven, moving time, non-movingtime, total time within one of the plurality of grid cells, time whenvehicle ignition is on, time when vehicle ignition is off, and vehiclevelocity. The usage information may be provided to a computing deviceand/or stored at a memory device.

Historical data may be collected from data sources that are bothinternal and external to an insurance company. In some cases, dataexternal to an insurance company may not be used because the data israndomly available within/across states and the data may have limitedadditional explanatory value.

Unless otherwise specified, average and statewide values may be treatedas constants for the purposes of the present invention if those valuesremain constant for some period of time. In other words, if two policiesthat cover properties at two locations at opposite corners of a largestate are rated at roughly the same time, any average or statewide valuethat remains the same in both rating calculations may be referred toherein as a constant. Similarly, if two policies that cover vehiclesused at opposite corners of a large state are rated at roughly the sametime, any average or statewide value that remains the same in bothrating calculations may be referred to herein as a constant.

When data is collected from outside an insurance provider, the data mayneed to be mapped into the coordinate grid in order to speed lookup andsimplify the required database queries. For example, the outside datamay be initially associated with postal ZIP codes, street addresses,school districts, or any other geographic locations and regions.

FIG. 5 illustrates the stylized town map overlaid with a coordinate gridand an alternative set of geographic boundaries, namely ZIP codes. Map500 illustrates at least three ZIP codes—ZIP A is contained within map500, and ZIP B and ZIP C each appear to extend beyond the edges of map500. Map 500 illustrates how postal ZIP codes may not coincide with thecoordinate lat/long grid. In one example, postal ZIP A is shown tocompletely encompass a number of grid cells, e.g., the grid cellsencompassing each of locations C and D. ZIP A also includes portions ofa number of other grid cells, e.g., a portion of the grid cellencompassing location E as well as a portion of route 525.

If data is associated with a ZIP code, rather than a grid cell, agrid-based query will not retrieve that data. For example, assume thatZIP A is associated with negligible levels of flammable vegetation, thusreducing the risk of a loss due to fire relative to homes in areas ofhigher levels of flammable vegetation. ZIP A may cover an urban area,while ZIP B may abut a forest. The data associated with ZIP A will becalled the ZIP A Data. In order to perform a grid-based query, it isdesirable to associate the ZIP A Data with one or more grid cellscovered by ZIP A.

FIG. 6 illustrates various approaches to mapping data into a grid-basedrepresentation scheme. Specifically, FIG. 6 includes a zoomed inrepresentation of a specific grid cell including location E and aportion of route 625, and further includes five separate abstract gridcells illustrating five means for mapping data into a grid cell. Gridcell 601 includes grid boundaries at N30.424, N30.423, W97.277, andW97.276, and center point 602 located at N30.4235, W97.2765. A boundaryline between ZIP A and ZIP D intersects the grid cell illustrated in map601. The GRID ID for this cell is 030423097276 assuming a GRIDresolution of three decimal points. Grid cells 603-606 illustrate fourexample methods of mapping data into the grid cell, origin mapping 603,center point averaging 604, four corner averaging 605, five pointaveraging 606, and pixel counting 607.

Using origin mapping 603, a data value associated with ZIP A will beassociated with cell 030423097276 because the origin of this cell(marked by a single dot) is within ZIP A. Using center point averaging604 results in the same mapping because center point 602 is also in ZIPA.

Using four corner averaging 605, a numeric data value is retrieved foreach corner of the grid cell with the average of the four points savedas the representative attribute of cell 030423097276. For example, ifZIP A is associated with an average vegetation coverage of 0.25 and ZIPB is associated with an average vegetation coverage of 0.60, then theaverage is 0.43 rounded to two decimal places. That average number wouldthen be stored in the database in association with cell 030423097276.

Using five point averaging 606, a value is retrieved for the centerpoint and each of the corners. The average of the five values is thenassociated with 030423097276. Here, the average of vegetation coveragevalues of 0.25, 0.25, 0.25, 0.60, and 0.60 is 0.39.

Using pixel counting 607, a portion of a raster map (e.g., from agraphic image file) is superimposed on a grid cell and aligned. Theraster map may be a graphical representation of a topographical map orother graphically represented information. In this approach, eachnon-zero pixel within the grid cell boundaries is counted and theaverage coverage of the grid cell is applied to the value represented bythe raster map. For example, in pixel counting 607, a total of 35 out of120 pixels are shaded in the raster map. A total of 29 pixels 608(shaded grey), covering approximately 24% of the 120 pixels in the gridcell, may be associated with a mask value of 0.045. A total of 6 pixels609 (with crosshatching), covering 5% of the grid cell, may beassociated with a mask value of 0.820. As a result, the average maskvalue is (0.045×0.24)+(0.820×0.05), or 0.052. In some embodiments, aminimum or maximum mask value may be associated with the grid cell.

Referring to FIGS. 7A and 7B, processes for coordinating data with aGRID map according to an embodiment of the invention is disclosedaccording to a flow chart. Geographic data in various formats areidentified in 701. The geographic data is being collected 702 fromvarious sources. The formats may include Vector (.shp), Tabular (.csv),Raster (tiff, .img). For the Vector data 703, a mathematical formula(e.g., using methods 603-606) may be applied 706 to compute a value fora grid based on related data points contained within each GRID cell.Alternatively, the Vector images 703 may be converted 706 into Rasterimages associating specific values to each pixel color (e.g., usingmethod 607). The collected tabular data 704 may be applied 707 to aVector definition and then one can follow a Vector data path.Alternatively, the tabular data 704 may be mathematically derived sothat the remaining undefined space between the base data points isobtained. The result of the process of step 707 may then be applied tostep 706 as described above or to step 709 as described below. TheRaster data 705 may be aligned 708 to the appropriate geographicprojection. Next, both the aligned Raster data 705 and the appliedtabular data 704 may be used to develop a range of values by samplingdata 709. The sample data may then be used to obtain average values bypixel and summarized at desired grid size at step 710. At this stage,all of the Vector 703, tabular 704, and Raster 705 data may then betransformed as steps 711 and 712. An input file is then created 713containing the developed geographic data along with a specific truncatedgrid identifier key. The data is then loaded into the database. Thedatabase is then made available 714. The data with grid identifier isthen made available 715 to modeling for analysis to determinepredictiveness. The modeling area compares new data against existinginsurance policy data to determine relevance against a particularcoverage for example. The data is also compared with existing ratingdata used in models to see if it is a complement. If the data isdetermined to be predictive, it is kept, otherwise the results arestored and the data is not used. The result is the predictive data 716.The predictive data may be fed 717 into an existing model that is usedto develop factors for rate making. The factors 718 are loaded intodatabases. A table is then created/updated 719 that contains thetruncated grid identifier and rating factors.

FIG. 7C illustrates an example of graphical information to be mappedinto a GRID-based data structure. While the following description refersspecifically to a brush fire risk, the techniques can be applied to anydata element in order to assign specific data values to GRID cells, suchas locations with high amounts of vehicle collisions, poor roads,uninsured drivers, for example. The insurer has been provided with datafor states that have exposure to Brush Fire risk. The Brush Fire Risk ata particular location considers several factors, including elevation,vegetation, land cover, etc., in addition to assigned risk values toareas in the state. The insurer has been provided maps of several statesthat show polygons and associated Brush Fire risk values. Risk valuesare 1, 2, 3, and 4. As the data provided is not by GRID, but rather bypolygons of different sizes and shapes in a graphical informationformat, this document explains several methods to assign polygon riskvalues to GRID cells. A common geographical information system (GIS)file format is addressed herein as a non-limiting examples: Geospatialboundary files in GSB format and MapInfo® files.

Data provided from certain sources are converted to GSB files. UserDefined Functions in insurer data stores utilize the GSB files toperform spatial look ups on latitude and longitude values andsubsequently return the risk values associated with the polygon thatcontains the specified latitude and longitude coordinates. A single GRIDcell covers a range of potential latitude and longitude coordinates.Thus, any GRID cell could contain coordinates that fall into one or morepolygons, and thus the algorithm could return one or more risk values.As a result, several approaches have been examined to determine the bestmethod to derive a single risk value for any given GRID cell.

Alternatively, data may be provided in, or converted to, a MapInfo®file, to determine the area covered by each GRID and the associated RiskValues. When multiple polygons span a GRID cell, the GRID cell isassigned the risk value associated with the polygon that is representedby the largest area in the GRID cell. For example, the map depicted inFIG. 7C shows a GRID cell that is spanned by multiple polygons.

In map 700, the grid cell with GRID ID ‘0483010798’ is spanned by sevenpolygons, three with a risk value of 1 (701) and four with a risk valueof 2 (702). The area covered by each polygon spanning GRID ‘0483010798’is given in Table 4.

TABLE 4 Polygon Risk Value Polygon Area 1 2 0.000490051 2 2 0.0002451843 2 0.000245026 4 2 0.003919830 5 1 0.005148140 6 1 0.005148140 7 10.284706000

Thus, upon summing the polygon areas, a risk value of 1 is assigned toGRID ‘0483010798’. An alternative related method could use the riskvalues and the polygon areas to develop a weighted average risk valuefor the GRID cell.

Gather Sufficient Data to Generate Actuarially Credible Results

With rating data stored in association with each GRID cell, the ratingprocess can begin with a data collection means. The process starts atthe target GRID cell(s), as that data is typically the most relevant tothe rating process. In many cases, a target GRID cell by itself will nothave adequate experience to produce an actuarially credible result sonearby data will be gathered as needed. According to one embodiment, therating system collects loss/exposure data in ring or border incrementsuntil the query scope first reaches a maximum distance (e.g., a selectedradius from the target GRID cell) or includes sufficient data to attainmaximum credibility. In an iterative approach, the system makes adetermination for each GRID cell ring or border increment as to whetherthe data gathered is adequate to produce a credible result. The ring orborder is incremented as needed up to the threshold ring or borderdistance.

Because most rating calculations will include data from GRID cellssurrounding the target GRID cell or bordering the sequence of targetGRID cells, calculations will tend to overlap. In other words,experience data in a given GRID cell will be used in the calculationsfor many nearby GRID cells. This data sharing helps to ensure a smoothertransition of LRFs across adjacent GRID cells. In certain embodiments,an exception to this process is that GRID cells near certain boundaries(e.g., political or geographical) prevents the system from crossingthose boundaries to gather data.

This iterative approach is illustrated in FIGS. 3A-3C and 4A-4B. FIGS.3A and 3B illustrate a target GRID cell, a first ring of cells, and asecond ring of cells. FIG. 3C illustrates a target sequence of targetGRID cells (e.g., route), a first border of cells, and a second borderof cells. FIGS. 4A and 4B show the stylized town map and overlaidcoordinate grid 400 as shown in FIG. 1. FIG. 4A shows an overlaid seriesof GRID cell rings according to certain embodiments of the presentinvention. Target GRID cell 401 is located near the center of the map. Afirst ring of GRID cells 402 is located around the target GRID cell 401.A second ring of GRID cells 403 is located around the first ring of GRIDcells 402. A third ring of GRID cells 404 is located around the secondring of GRID cells 403. For this particular GRID ring configuration,location F resides within the target GRID cell 401. FIG. 4B shows anoverlaid GRID cell border according to certain embodiments of thepresent invention. The sequence of target GRID cells 405 is located nearthe center of the map and include those GRID cells that include orcontain at least a portion of the route 425. A first border of GRIDcells 406 is located around the sequence of target GRID cells 405 anddenoted by GRID cells having diagonal lining within. Although not shown,it is to be understood that additional borders of GRID cells may belocated around the first border of GRID cells 406. For this particularGRID border configuration, the route 425 resides within the sequence oftarget GRID cells 405. Thus, FIGS. 3A-3C and 4A-4B illustrate how theuse of GRID cell ring and border increments may be used to gather anactuarially credible amount of data needed to rate a policy in thetarget GRID cell and/or the sequence of target GRID cells.

Credibility

This process of aggregating sufficient data to make a reliable andaccurate risk assessment has been discussed above. In certainembodiments, this process is aided by the use of a well-knowncredibility formula, which is:C=ZR+(1−Z)H

-   -   where:    -   R is the mean of the current observations (for example, the        data)    -   H is the prior mean (for example, the estimate based on the        actuary's prior data and/or opinion    -   C is the [insurance rate]    -   Z is the credibility factor, satisfying 0≤Z≤1.        T. Herzog, Credibility: The Bayesian Model Versus Büthlmann's        Model, 41 Transactions of Society of Actuaries 43-88, at        43 (1989) (herein incorporated by reference). The credibility        factor Z is defined by:

$\begin{matrix}{Z = \frac{n}{\left( {n + k} \right)}} & (4.1)\end{matrix}$and satisfies 0≤Z≤1; also, n is the number of trials or exposure units,and

$\begin{matrix}{k = \frac{{expected}\mspace{14mu}{value}\mspace{14mu}{of}\mspace{14mu}{the}\mspace{14mu}{process}\mspace{14mu}{variance}}{{variance}\mspace{14mu}{of}\mspace{14mu}{the}\mspace{14mu}{hypothetical}\mspace{14mu}{means}}} & (4.2)\end{matrix}$Herzog, at 53. The basic principle is that the credibility of the rateincreases with quantity n or N_(z) of relevant expected claims orexposure units. The credibility factor may also be calculated as afunction of the expected number of claims (N_(z)) the full credibilitystandard (N_(f)).

$z = \sqrt{\frac{Nz}{Nf}}$G. Ventner, “Classical Partial Credibility with Application to Trend,”Proceedings of the Casualty Actuarial Society Casualty Actuarial Societyat 31 (1986) (herein incorporated by reference).Adjust Historical Data for Distance

Because geographically distant data may be less relevant to the ratingprocess than geographically near data, certain embodiments of thepresent disclosure may include a step of applying a distance weightingalgorithm to discount or devalue data as a function of distant from thetarget cell or the sequence of target cells. Thus, the method of theseembodiments relies more heavily on the immediately adjacent GRID cellexperience than on distant GRID cell experience. However, this weightingprocess may be more relevant to certain perils or coverages than others.

In the case of automobile insurance, calculations may be carried out oneach major coverage to develop an initial indicated LRF by coverage.These amounts are then credibility weighted to develop the finalindicated LRFs by coverage.

For any insurance product with low exposure, special considerations maybe warranted. For example, losses may be capped in various ways tomitigate the impact of shock losses or events. In some embodiments,losses may be capped based on the dollar amount incurred or paid over aspecified period of time. For example, losses incurred or paid over aday, week, year or any other relevant period of time can be used in theanalysis.

In other embodiments, individual claims may be capped at a specifiedthreshold or otherwise removed to limit their impact on the finalindicated LRFs.

In other embodiments, it may further be appropriate to cap actual lossesper policy, i.e., pure premium, at a specified multiple of thecomplement of credibility. Table 5 provides an example.

TABLE 5 Data Item Value Loss $167,090 Common Risk Exposure 10.83 LossPure Premium $15,434.11 Statewide Loss Pure Premium $81.63 Capped LossPure Premium $326.52 Credibility Standard 1,250.00 Claims 4.00Credibility Factor 0.0566 Model Loss Pure Premium 90.20 Relativity11.738 Relativity with Capped Pure Premium 1.269

Losses in the above table and throughout this document may or may notinclude loss expenses and may or may not include losses due tocatastrophic events. Losses and loss expenses may also be on a paid,case incurred, or total incurred basis.

Apply Credibility Weighting

A further step of the process may be to apply credibility weightingusing an appropriate credibility complement. Peril credibility standardsmay be developed based on accepted methodologies. Credibility standardsmay be based on confidence intervals and acceptable error thresholds.

Calculate Pure Premiums

Yet another step of the process may be to calculate pure premiums foreach target GRID cell. For example, expected pure premium may becalculated by peril or coverage and then aggregated to an all-peril orall-coverage basis. In addition, premiums may be calculated for eachGRID cell within a sequence of GRID cells and then aggregated, averaged,adjusted to a coverage basis.

According to an embodiment of the invention, pure premiums may becalculated using a provision that represents a long term average lossper exposure or alternatively be set to another appropriate provision,such as setting them equal to statewide pure premiums. Then, the perilor coverage pure premiums may be aggregated to an all-peril orall-coverage basis.

According to an embodiment of the invention, exposure may be adjusted toaccount for any distributional differences in rating variables across astate or geographic area. Peril or coverage specific adjustment factorsmay be used. An average GRID cell adjustment may be calculated relativeto an average statewide adjustment. For example, Table 6 illustrates anexample process to develop exposure adjustments for two GRIDs.

TABLE 6 Exposure Deductible (1) Road Type (2) GRID Count ValueAdjustment Value Adjustment (¹) * (²) 0369010450 10 $500 1.25 City 1.001.2500 0369010450 20 $500 1.25 Highway 0.85 1.0625 0369010450 20 1% 1.00City 1.00 1.0000 GRID Weighted Avg. 1.0750 0369010451 20 $500 1.25 City1.00 1.2500 0369010451 10 $500 1.25 Highway 0.85 1.0625 0369010451 40$1,000 1.05 Highway 0.85 0.8925 0369010451 25 1% 1.00 City 1.00 1.00000369010451 15 2% 0.90 Highway 0.85 0.7650 GRID Weighted Avg. 0.9800State Weighted Avg. 1.0097The above example assumes only two GRIDs exist in the state. Table 7provides the weighted adjustment and the exposure adjustments for thetwo illustrative GRIDs of Table 6.

TABLE 7 Peril Exposure GRID Weighted Adjustment Adjustment 03690104501.0750 1.0647 0369010451 0.9800 0.9706 Statewide 1.0097 1.0000Develop Location Rating Factor

A further step of the process may be to develop an indicated LocationRating Factor.

Process Steps

FIG. 8A provides a flow chart, which is illustrative of an embodiment ofthe invention. Flow chart 800 illustrates an example flow of steps takento develop a pure premium. Data is input 801 into a database, so that atable of all the GRID cells for a particular state is maintained in aseparate database table. Other data may also be input into the system tobe used for the analysis, such as grid level data, policy level data,and latitude and longitude level data. The location of the relevantperson, property or thing being insured is then determined 802 and aninitial selection of the corresponding GRID cell is identified.Longitudinal and latitudinal coordinates are then truncated 803depending on how the GRID has been truncated for this area. The ring ofGRID cells is set or initialized 804 to zero, which will result in aquery for data in the target cell. In some embodiments, the ring size isinitialized to one after data has been queried for the target cell.

Data filters may be set 805 to restrict the query to relevant data.Filters may be incorporated into a database query string, or may beapplied to the query results. Filters may, for example, restrict thequery to data relevant to:

-   -   types of claim, e.g., auto claims;    -   specific perils, e.g., data associated with a fire hazard,        collision;    -   types of properties and/or vehicles, e.g., single-family        residence, commercial property, or vacation home; automobile,        truck;    -   classifications of properties and/or vehicles, e.g., standard        homes, mobile homes, or high-rise homes, sedan, SUV;    -   features of properties, e.g., construction material, size,        construction cost, waterfront, elevation, or neighborhood        characteristics, vehicle safety features, e.g., anti-lock        braking system, air-bags; or    -   geographic or political subdivisions, e.g., limited to a state,        county, or regulatory district.

The last item listed is of some importance with certain regulatedproducts. Some insurance and financial services products are regulatedby local or regional governmental entities. For example, regulation ofcertain insurance products is performed by state agencies in the UnitedStates. These entities may be referred to as regulators. In someembodiments, the database may contain generalized rules and criteriathat apply to all policies and specific rules associated with aparticular regulator. This arrangement of data would allow, for example,a single computational process to generate a rate for an insurancepolicy for a property in any state.

Data corresponding to the ring of GRID cells is queried 806. To query806 the data, a temporary table of data is created for the data existingat the latitude/longitude level. The temporary data is summarized at thelevel of desired truncation and maintained until the completion of theanalysis, since it is reused in each iteration. A query uses the inputcell and determines via query which of the other cells in the state fallwithin the ordered rings up to the maximum distance, for example, 30miles. Once the data for the other GRID cells are ordered, they areiterated through until a level of credibility is met, using thesummarized data. Query 806 produces a target set of data that isassociated with the target cell and/or current ring.

The target set of data is then processed 807, once the level is met thevalues retrieved and weighted based on the GRID ring, by loading thedata into an overall calculation that develops a value for Ring 0. Adetermination 808 is then made as to whether the processed data isgreater than or equal to the maximum credibility or target credibility.If the credibility of the processed data is not greater than or equal tothe maximum credibility or target credibility, then a determination 809is made as to whether the size of the ring of GRID cells has reached amaximum. If the size of the ring of GRID cells has not reached amaximum, then the ring size is incremented 810 by one and the processreturns to the query step 806. If the size of the ring of GRID cells hasreached a maximum, then a sparse data handler is used to calculate arate. The process is continued for each GRID cell within a set oflatitude/longitude boundaries, for example, defined by a state. If theprocessed data of step 807 is determined 808 to be greater than or equalto the maximum credibility or target, then a rate is adjusted 812. Insome cases, the resulting rate values created are just a portion of thepotential rate that would be presented to a customer because there aremany factors that may come into play in calculating the final rate.Additional steps may also be added to modify the rate before it iscommunicated to a customer. Finally, a rate may then be communicated 813to the potential customer for whom the policy rate quote has beenrequested.

Sparse data handler 811 provides a mechanism for addressing ratingrequests that cannot be completed normally as a result of insufficientrating data. In certain embodiments, sparse data handler 811 returns anerror message signaling an inability to calculate a pure premium. Insome embodiments, the error message may include the calculated purepremium information along with the credibility factor.

Example

As a further example of one aspect of the invention, specific data isprovided to illustrate a method having the following steps: collectingdata according to GRID rings, determining whether rings have enoughhistorical data to provide actuarially credible results, adjusting thedata for distance, and applying credibility weighting. The calculationsherein represent a rating means, according to certain embodiments of thepresent disclosure.

First, historical data may be collected for GRID ring 0 (target GRID).Table 8 illustrates a data collection for claims and loss and lossexpenses (referred to below as Loss Amount) for an exemplary targetGRID.

TABLE 8 GRID Ring Claims Loss Amount 0 8 $20,640 Total 8 $20,640

Second, historical data may be collected for GRID ring 1. This GRID ringmay be for an interval ring having an inside and outside radius atselected distances from the target GRID cell. Table 9 illustrates a datacollection for claims and loss and loss expenses for the exemplarytarget GRID and the exemplary GRID ring 1.

TABLE 9 GRID Ring Claims Loss Amount 0 8 $20,640 1 23 $64,519 Total 31$85,159

Historical data may also be collected for GRID ring 2. This GRID ringmay be for an interval ring having an inside and outside radius atselected distances from the target GRID cell, but lying outside andsurrounding GRID ring 1.

Table 10 illustrates a data collection for claims and loss and lossexpenses for the exemplary target GRID, the exemplary GRID ring 1, andthe exemplary GRID ring 2.

TABLE 10 GRID Ring Claims Loss Amount 0 8 $20,640 1 23 $64,519 2 93$247,717 Total 124 $332,876Loss and exposure data may be collected for additional interval ringsuntil maximum credibility is obtained or a maximum distance is reached.In this illustrative example, maximum credibility is obtained with datafor only seven GRID rings being collected.

Table 11 illustrates a data collection for claims and loss and lossexpenses for the exemplary target GRID (GRID ring 0) and the exemplaryGRID rings 1-6.

TABLE 11 GRID Ring Claims Loss Amount 0 8 $20,640 1 23 $64,519 2 93$247,717 3 160 $404,077 4 206 $581,755 5 348 $955,226 6 414 $1,095,219Total 1,252 $3,369,154

While the loss and exposure data above is illustrative for one peril orcoverage, data may be collected in the same manner for any peril orcoverage.

Third, a distance weighting factor may be applied to the loss andexposure data. For this particular example, as shown in FIGS. 9A and10A, the weighting factor is about 1 for GRID rings 0-3 and GRID rings0-8, respectively, and then the weighting factor decreases towards zero.Weighting factors may follow any function or curve shape. The weightingfactor may even be truncated or it may increase with distance from thetarget GRID.

Fourth, credibility weighting may be applied using an external datamodel result, statewide pure premium, prior indicated factor, factorimplied by current rate, or any other relevant value as the complement.Credibility standards may be based on varying confidence intervals andacceptable error thresholds.

Fifth, pure premiums may be calculated by peril for expected loss andloss expenses. The loss and loss expenses may be divided by the exposureto determine the loss and loss expenses pure premium. Illustrativeresults are provided in Table 12 for one peril.

TABLE 12 Data Item Value Loss $818,851.12 Common Risk Exposure $4,280.14Loss Pure Premium $191.31 Capped Loss Pure $191.31 Premium CredibilityStandard 1,250 Claims 313 Credibility Factor 0.50 Model Loss Pure$186.29 Premium Expected L&ALAE PP $188.80

An expected loss and loss expenses pure premium may also be calculatedbased on a provision that represents a long term average loss perexposure or alternatively be set to another appropriate provision, suchas setting them equal to statewide pure premiums.

Sixth, pure premiums for the various perils or coverages may beaggregated to an all—peril or all—coverage basis.

Seventh, the all-peril or all-coverage pure premiums can then be used toderive an indicated Location Rating Factor.

Example—Vehicle GRID (Geographic Rating ID) Rating Methodology

According to one exemplary application of the invention, a vehicle GRIDrating methodology is illustrated. The calculations herein represent arating means, according to certain embodiments of the presentdisclosure. A GRID border experience is included in the calculations fora sequence of target GRID cells until one of the following criteria ismet: (1) reach full or maximum credibility; (2) reach the maximumdistance; or (3) reach maximum change in additional variable (e.g.,percent change in population density). The GRID border experience may beadjusted based on a distance factor. The distance factor may bedetermined based on a linear or non-linear function of maximum truncateddistance from the sequence of target GRID cells. The same distancefactor may be applied to each cell within a given border.

According to a methodology for the research plan, a GRID cell experienceborder analysis may be conducted based on an insurance provider'shistorical data to predict non-catastrophe expected pure premiums bycoverage. In FIG. 8B, an exemplary process for implementing anoptimization program may evaluate the following coverage by: (1) maximumcredibility assigned to a target cell's experience area; (2) distanceweighting functions applied to border experience; (3) loss experienceperiod used to develop a target cell's experience and the weight givento each year's experience; and (4) impact of changes in additionalvariables.

In FIG. 8B, the exemplary process for implementing a rating methodologyfor a vehicle policy is depicted in the flow diagram 820 wherein acoordinate grid including a plurality of grid cells is defined (block821). A location rating factor for one or more of the defined grid cellsis determined (block 822). The location rating factor may includevehicle usage information within the grid (block 823), which may beattained by a positioning module, e.g., GPS device, operatively coupledto the vehicle. The positioning module may also be incorporated within asmartphone associated with the vehicle.

The usage or operating information of the vehicle with respect to one ormore grid cells may include: location(s), e.g., route(s) travelled bythe vehicle, the average velocity of the vehicle, the maximum velocityof the vehicle, the amount of time the vehicle is within a particulargrid cell, the distance travelled by the vehicle, the amount of time thevehicle was moving, the amount of time the vehicle was or idle (e.g.,parked), the amount of time the engine of the vehicle is running, etc.One or more of the factors may be weighted based on the usage of thevehicle within the grid and/or within one or more particular grid cells(block 824). Other data that may be considered along with the usageinformation of the vehicle, include: age and driving experience of thevehicle operator or vehicle owner; weather and climate data of thegeographic area associated with the grid; historical data; census data;quantity, frequency, and severity of insurance claims; drivingstatistics of the vehicle operator or vehicle owner and other drivers(insured and uninsured); road statistics; crime data; type andclassification of the vehicle; time and date; population density,weather data; historical data, traffic conditions experienced by thevehicle, police reports, etc. One or more of the vehicle's usageinformation and other data may be applied to determine various coveragesof liability, such as bodily injury, property damage, collision, otherthan collision, roadside assistance, medical, uninsured and underinsuredmotorists, etc. This information with respect to each grid cell withinthe sequence of grid cells may then be utilized, separately or invarious combinations, to underwrite the vehicle insurance policy (block825). In particular, one or more grid cells within the route's thesequence of grid cells may be used, not used, or weighted in thedetermination of the vehicle insurance policy.

The information with respect to one or more grid cells within thesequence of grid cells may also be used to compare against an existingvehicle insurance policy. For example, the usage information of thevehicle may be automatically compared to usage criteria of the insurancepolicy associated with the vehicle. If a discrepancy between the usageinformation of the vehicle and the usage criteria of the vehicleinsurance policy is detected, a notification maybe be automaticallygenerated and sent to a user, for example, an owner of the vehicleand/or an insurance agent associated with the vehicle. The notificationmay advise of the detected discrepancy of the vehicle's usage withrespect to the insurance policy covering the vehicle.

According to another exemplary application of the invention, a vehicleGRID rating methodology is illustrated. The calculations hereinrepresent a rating means, according to certain embodiments of thepresent disclosure. A GRID ring experience is included in thecalculations for each target GRID cell within a sequence of target GRIDcells until one of the following criteria is met: (1) reach full ormaximum credibility; (2) reach the maximum distance; or (3) reachmaximum change in additional variable (e.g., percent change inpopulation density). The GRID ring experience may be adjusted based on adistance factor. The distance factor may be determined based on a linearor non-linear function of maximum truncated distance from the targetGRID cell. The same distance factor may be applied to each cell within agiven ring.

According to a methodology for the research plan, a GRID cell experiencering analysis may be conducted based on an insurance provider'shistorical data to predict non-catastrophe expected pure premiums bycoverage. An optimization program may evaluate the following coverageby: (1) maximum credibility assigned to a target cell's experience area;(2) distance weighting functions applied to ring experience; (3) lossexperience period used to develop a target cell's experience and theweight given to each year's experience; and (4) impact of changes inadditional variables.

Data and methodology for this vehicle example are provided in FIGS.9A-9F. FIG. 9A provides representative GRID distance weighting values,for a vehicle example. FIG. 9B provides an overview of the methodologyfor a vehicle example. FIG. 9C provides the GRID ring level data for avehicle example. FIG. 9D provides the GRID cell level data for a vehicleexample. FIG. 9E provides methodologies for calculating distance betweentwo latitude and longitude coordinate pairs in a vehicle example. FIG.9F provides the results of the distance calculations for a vehicleexample.

System

FIG. 10 illustrates an exemplary computing and information handlingsystem according to one embodiment of the invention. System 1100comprises one or more computers 1110. Each computer 1110 may comprise acentral processing unit (CPU) 1101, a user interface 1102, a memory1103, and a network interface 1104. The memory 1103 comprises one ormore application software modules and one or more internal data stores.System 1100 further comprises a communications network 1105 and externaldata stores 1106.

Computer 1110 may be any type of general purpose or specialized computersystem. In some embodiments computer 1110 may be a personal computer(e.g., an X86-based computer) running an operating system such as UNIX™,OSX™, or WINDOWS™. In some embodiments computer 1110 may be a server orworkgroup class system such as those offered by IBM™, HP™, COMPAQ™, orORACLE™. In other embodiments, computer 1110 may be a mainframe systemsuch as an IBM ZSERIES™ mainframe. System 1100 may comprise aheterogeneous or homogeneous network of computers 1110. In someembodiments, computer 1110 may be a mobile device such as a laptop orsmart phone.

CPU 1101 may be any general purpose processor including ARM™′ X86, RISC,and ZIO™. Memory 1103 may be any form or combination of volatile and/ornon-volatile tangible computer readable medium including semiconductormemory (e.g., RAM, ROM, flash, EEPROM, and MRAM), magnetic memory (e.g.,magnetic hard drives, floppies, and removable drive cartridges), opticalmemory (e.g., CD-ROM, DVD-ROM, BLURAY™ ROM, and holographic storage).Memory 1103 provides transient and/or persistent storage of ApplicationSoftware Modules and Internal data. Memory 1103 also provides storagefor operating system software including device drivers and systemconfigurations. Network interface 1104 provides data interconnection—viacommunications network 1105—between computers 1110 and external data1106.

Internal data may comprise data stored as bitmaps, vectors, objects,tables, and/or files. Internal data may be associated with GRID cells.Internal data may be comprehensive or may be a subset of data limited toa particular geographical region. In some embodiments, internal data mayinclude a limited set of GRID cell data to allow an agent to performrating operations using a mobile device (e.g., a mobile device runningIOS™, ANDROID™, PALMOS™, or WINDOWS™). The data set may be limited toGRID cells in a given metropolitan area, for example. In someembodiments, internal data may be limited to one or more states where anagent is licensed to write policies. In some embodiments, internal datamay include a limited set of GRID cell data to allow an agent to rateonly certain predetermined perils (e.g., auto and homeowners, but notcommercial fire).

Application Software modules comprise software or firmware instructionsand configuration information that provides instructions to CPU 1101 toperform the steps of the methods, procedures, and functions disclosedherein. Application Software may be implemented in a compiled and/orinterpreted environment. In some embodiments, Application Softwaremodules may be implemented in a high-level programming language such asCOBOL, FORTRAN, C, Cd-F, SmallTalk, JAVA™, C#, assembly language, JAVA™server pages (JSP), application server pages (ASP), or VISUAL BASIC™.

Communications network 1105 may be a heterogeneous or homogenous set ofphysical mediums (e.g., optical fiber, radio links, and copper wires)and protocol stacks (e.g., ETHERNET™, FDDI, GSM, WIMAX™, LTE, USB™,BLUETOOTH™, FIOS™, 802.11, and TCP/IP.

External data 1106 may be any form of data source. In some embodiments,external data 1106 is received on an optical disk and imported into aninternal data store for further processing. In some embodiments,external data 1106 is an external data store hosted on a computeraccessible via communications network 1105. External data may includeroute information travelled by a vehicle and attained through apositioning module 1107, e.g., GPS device, operatively coupled to thevehicle. Alternatively, the route information of the vehicle attained bythe positioning module 1107 may be provided to the computing device1110. External data 1106 may be available for on demand retrieval or maybe pushed by a data provider. External data 1106 may be transferred tocomputer 1110 in whole or in part. This transfer may be periodic, ondemand, or as changes occur.

System 1100 may be configured such that one or more of the computers1110 is configured with hardware and software that enables it to collectinternal and/or external data and map that data into an internal datastore associated with GRID cell identifiers. One or more computers 1110are further configured to collect data for each target GRID cell and itsadjacent GRID rings or sequence of target GRID cells and borderingcells, determine whether GRID rings and/or borders have enoughhistorical data to provide actuarially credible results for each targetGRID cell(s), adjust the data of the GRID rings and/or borders fordistance, apply credibility weighting, and calculate pure premiums foreach GRID cell.

The system 1100 is configured such that one or more of the computers1110 is configured to receive an input request through a user interface1102 for a rate quote for an insurance policy covering a risk associatedwith a particular location and/or route(s). The computer 1110 firstobtains the latitude and longitude coordinates for the location viadirect input (e.g., via user interface 1102), an address cross-referencetable (e.g., a geolocation database), direct location coordinates from apositioning module 1107, e.g., GPS device, for one or more routes, etc.The computer 1110 then executes a look-up process whereby the latitudeand longitude coordinates for the location and/or routes are used toidentify a target GRID cell and/or sequence of target GRID cells in theGRID cell network. The computer 1110 then generates pure premiums forthe target GRID cell and/or sequence of target GRID cells and returns arate quote for the location and/or route(s).

The disclosure of U.S. patent application Ser. No. 13/226,785, filedSep. 7, 2011, entitled “Systems and Methods for Grid-Based InsuranceRating,” is hereby incorporated by reference.

For the purposes of this disclosure, the term exemplary means exampleonly. Although the disclosed embodiments are described in detail in thepresent disclosure, it should be understood that various changes,substitutions and alterations can be made to the embodiments withoutdeparting from their spirit and scope.

What is claimed is:
 1. A method executed on a programmed computer systemto automatically determine risk of a vehicle based on movement andlocation of the vehicle, the programmed computer system having aprocessor and a non-transitory, tangible computer readable mediumcommunicatively coupled to the processor and storing instructionsexecutable by the processor to perform the method comprising: storing,at a database, vehicle risk data associated with a plurality of gridcells corresponding to a geographic area; receiving, at the processor, aquery for a vehicle risk of the vehicle; monitoring, via a globalpositioning system (GPS) device disposed with the vehicle, usage of thevehicle while travelling within the geographic area; receiving, at theprocessor, sensor data attained from the GPS device, the sensor dataincluding usage information of the vehicle including a route travelledby the vehicle within the geographic area, wherein the usage informationincludes a moving amount of time the vehicle moves along the route and astationary amount of time the vehicle does not move along the route;determining, via the processor, each of the plurality of grid cells thatencompasses at least a portion of the route travelled by the vehicle;querying, via the processor, the database to attain a set of vehiclerisk data associated with each of the determined plurality of grid cellsthat encompasses at least a portion of the route travelled by thevehicle; receiving, at the processor, the queried set of vehicle riskdata associated with each of the plurality of grid cells thatencompasses at least a portion of the route travelled by the vehicle;calculating, via the processor, a location rating factor based on themoving amount of time the vehicle moves along the route, the stationaryamount of time the vehicle does not move along the route, and thereceived set of vehicle risk data associated with each of the pluralityof grid cells that encompasses at least a portion of the route travelledby the vehicle; incorporating, via the processor, the usage informationreceived within the sensor data with the vehicle risk data stored in thedatabase and associated with each of the determined plurality of gridsthat encompasses the at least a portion of the route travelled by thevehicle; and communicating, via the processor, the calculated locationrating factor to a user.
 2. The method of claim 1, wherein the usageinformation of the vehicle further includes one or more of thefollowing: location, miles driven, total time within one of theplurality of grid cells, time when vehicle ignition is on, time whenvehicle ignition is off, average vehicle velocity, maximum vehiclevelocity, and change in vehicle velocity.
 3. The method of claim 1,wherein the route travelled by the vehicle includes a plurality ofcoordinate pairs, each coordinate pair including a longitude value and alatitude value.
 4. The method of claim 1, wherein the set of vehiclerisk data includes one or more of the following: census data, crimedata, weather data, historical data, and other data, such as quantity ofvehicle insurance claims, severity of vehicle insurance claims,frequency of vehicle insurance claims, driving statistics, roadstatistics, time, date, or population density.
 5. The method of claim 1,wherein each of the grid cells of the plurality of grid cells comprisinga four-sided area defined by latitude and longitude values of acoordinated grid system defining a geographic size of that particularcoordinated grid cell.
 6. The method of claim 5, further comprisingadjusting the geographic size of one or more grid cells by truncatingthe number of digits in the latitude and longitude values that definethat grid cell.
 7. The method of claim 1, further comprising:automatically comparing the usage information of the vehicle to usagecriteria of a vehicle insurance policy; automatically detecting adiscrepancy between the usage information of the vehicle and the usagecriteria of the vehicle insurance policy; automatically notifying a userof the detected discrepancy between the usage information of the vehicleand the usage criteria of the vehicle insurance policy.
 8. A methodexecuted on a programmed computer system to automatically determine riskof a vehicle based on movement and location of the vehicle, theprogrammed computer system having a processor and a non-transitory,tangible computer readable medium communicatively coupled to theprocessor and storing instructions executable by the processor toperform the method comprising: storing, at a database, vehicle risk dataassociated with a plurality of grid cells corresponding to a geographicarea; receiving, at the processor, a query for a vehicle risk of thevehicle; monitoring, via a global positioning system (GPS) devicedisposed with the vehicle, usage of the vehicle while travelling withinthe geographic area; receiving, at the processor, sensor data attainedfrom the GPS device, the sensor data including usage information of thevehicle including a route travelled by the vehicle within the geographicarea, wherein the usage information includes a moving amount of time thevehicle moves along the route and a stationary amount of time thevehicle does not move along the route; determining, via the processor, asequence of coordinate pairs that are included in the route travelled bythe vehicle, each coordinate pair including a latitude value and alongitude value; querying, via the processor, a database to attain a setof vehicle risk data associated with a sequence of grid cells thatencompasses each coordinate pair in the sequence of coordinate pairs;receiving, at the processor, the queried set of vehicle risk dataassociated with each of the plurality of grid cells within the sequenceof grid cells that encompasses each coordinate pair in the sequence ofcoordinate pairs; calculating, via the processor, a location ratingfactor based on the moving amount of time the vehicle moves along theroute, the stationary amount of time the vehicle does not move along theroute, and the received set of vehicle risk data associated with each ofthe grid cells within the sequence of grid cells that encompasses eachcoordinate pair in the sequence of coordinate pairs; incorporating, viathe processor, the usage information received within the sensor datawith the vehicle risk data stored in the database and associated witheach of the determined plurality of grids that encompasses the at leasta portion of the route travelled by the vehicle; and communicating, viathe processor, the calculated location rating factor to a user.
 9. Themethod of claim 8, further comprising: querying, via the processor, adatabase to attain a supplemental set of vehicle risk data associatedwith grid cells bordering the sequence of grid cells that encompasseseach coordinate pair in the sequence of coordinate pairs; receiving, atthe processor, the queried supplemental set of vehicle risk dataassociated with grid cells bordering the sequence of grid cells thatencompasses each coordinate pair in the sequence of coordinate pairs;and calculating, via the processor, a supplemental location ratingfactor based on the usage information, the received set of vehicle riskdata associated with each of the grid cells within the sequence of gridcells that encompasses each coordinate pair in the sequence ofcoordinate pairs, and the received set of supplemental vehicle risk dataassociated with grid cells adjacent to the sequence of grid cells thatencompasses each coordinate pair in the sequence of coordinate pairs;and communicating, via the processor, the calculated supplementallocation rating factor to the user.
 10. The method of claim 9, whereinthe usage information of the vehicle further includes one or more of thefollowing: location, miles driven, total time within one of theplurality of grid cells, time when vehicle ignition is on, time whenvehicle ignition is off, average vehicle velocity, maximum vehiclevelocity, and change in vehicle velocity.
 11. The method of claim 10,wherein the set of vehicle risk data includes one or more of thefollowing: quantity of vehicle insurance claims, severity of vehicleinsurance claims, frequency of vehicle insurance claims, drivingstatistics, road statistics, crime rate, theft rate, weather condition,time, date, or population density.
 12. The method of claim 11, whereinthe set of supplemental vehicle risk data includes one or more of thefollowing: quantity of vehicle insurance claims, severity of vehicleinsurance claims, frequency of vehicle insurance claims, drivingstatistics, road statistics, crime rate, theft rate, weather condition,time, date, or population density.
 13. The method of claim 10, whereineach coordinate pair includes a non-truncated longitude value and anon-truncated latitude value.
 14. The method of claim 8, wherein eachparticular grid cell comprising a four-sided area defined by latitudeand longitude values of a coordinated grid system defining a geographicsize of that particular coordinated grid cell.
 15. The method of claim14, further comprising adjusting the geographic size of one or more gridcells by truncating the number of digits in the latitude and longitudevalues that define that grid cell.
 16. The method of claim 8, furthercomprising: automatically comparing the usage information of the vehicleto usage criteria of a vehicle insurance policy; automatically detectinga discrepancy between the usage information of the vehicle and the usagecriteria of the vehicle insurance policy; automatically notifying a userof the detected discrepancy between the usage information of the vehicleand the usage criteria of the vehicle insurance policy.
 17. A computersystem for automatically determining risk of a vehicle based on movementand location of the vehicle, the computer system comprising: a processorcommunicatively coupled to a user interface; a coordinate grid systemassociated with a geographic area, the coordinate grid system includinga plurality of grid cells, each grid cell comprising a four-sided areadefined by latitude and longitude values; a database communicativelycoupled to the processor, the database storing vehicle risk dataassociated with the plurality of grid cells; a global positioning system(GPS) device disposed with the vehicle and communicatively coupled tothe processor for monitoring usage of the vehicle; a non-transitory,tangible computer readable memory communicatively coupled to theprocessor; and a set of computer readable instructions stored in thenon-transitory computer readable memory and when executed by theprocessor, are configured to: receive, at the processor, a query for avehicle risk of the vehicle; monitor, via the GPS device, usage of thevehicle while travelling within the geographic area; receive, at theprocessor, sensor data attained from the GPS device, the sensor dataincluding usage information of the vehicle including a route travelledby the vehicle within the geographic area, wherein the usage informationincludes a moving amount of time the vehicle moves along the route and astationary amount of time the vehicle does not move along the route;determine, via the processor, each of the plurality of grid cells thatencompasses at least a portion of the route travelled by the vehicle;query, via the processor, the database to attain a set of vehicle riskdata associated with each of the determined grid cells that encompassesat least a portion of the route travelled by the vehicle; receive, atthe processor, the queried set of vehicle risk data associated with eachof the plurality of grid cells that encompasses at least a portion ofthe route travelled by the vehicle; calculate, via the processor, alocation rating factor based on the moving amount of time the vehiclemoves along the route, the stationary amount of time the vehicle doesnot move along the route, and the received set of vehicle risk dataassociated with each of the plurality of grid cells that encompasses atleast a portion of the route travelled by the vehicle; incorporate, viathe processor, the usage information received within the sensor datawith the vehicle risk data stored in the database and associated witheach of the determined plurality of grids that encompasses the at leasta portion of the route travelled by the vehicle and communicate, via theprocessor, the calculated location rating factor to a user.
 18. Thecomputer system of claim 17, wherein the usage information of thevehicle further includes one or more of the following: miles driven,moving time, non-moving time, total time within one of the plurality ofgrid cells, time when vehicle ignition is on, time when vehicle ignitionis off, average vehicle velocity, maximum vehicle velocity, and changein vehicle velocity.
 19. The computer system of claim 18, wherein theroute includes a plurality of coordinate pairs, each coordinate pairincluding a non-truncated longitude value and a non-truncated latitudevalue.
 20. The computer system of claim 19 wherein the four-sided areaof each grid cell being defined by truncated latitude and longitudevalues, and wherein a number of digits in the truncated latitude andlongitude values define a geographic size of that grid cell.
 21. Thecomputer system of claim 17, wherein the vehicle risk data associatedwith the plurality of grid cells includes one or more of the following:quantity of vehicle insurance claims, severity of vehicle insuranceclaims, frequency of vehicle insurance claims, driving statistics, roadstatistics, crime rate, theft rate, weather condition, time, date, orpopulation density.